1. Field of the Invention
The present invention relates to the field of diagnostic assessment and monitoring of complex biological and physical structures, including, but not limited to, regions of the body which experience a change in structure. The change in structure includes, but is not limited to, changes during disease (e.g., angiogenesis) or changes between different functional modes (e.g., mammary glands before, during and after pregnancy).
2. Prior Art
Glossary: The following terms will be used throughout the text.                1. Prism: A prism is an elongate volume of material from which a signal is measured. The signal varies as a function of position along the prism. Although the form of prism generally referred to in the text has a rectangular cross section, in general it may include shapes of any arbitrary cross-section. Additionally, although the measurement of signal generally referred to in the text is a modification of current MRI techniques, as it is evident to anyone skilled in the art, the signal could also be gathered from a prism volume using other techniques.        2. Profile: A profile is the signal as a function of position along a single direction within a prism. Profiles taken from materials with heterogeneous signal intensity will in general be non-constant and represent a measure of the amount of signal generating material with position in the prism. As an example, this may be presence or absence of material, or material density. However, it could measure any number of properties of the material, but the salient point that the signal versus position gives a measure of some physical property with position along the prism. As is evident to one skilled in the art, the profile could represent any measurable physical property which varies with position.        3. Segment: In order to perform measurements on the variation of the profile signal with position, a portion of the profile of finite extent is used. This portion of profile may be less than or equal to the entire profile length, and is termed a “segment”.        4. Spatial frequency: In signal processing, many techniques exist for estimating the frequency content of a signal. Generally these signals vary with time. When these techniques are applied to a signal which varies with position, rather than time, the analogue of frequency content is a measure of the frequency of spatial variations along the profile. For the purposes of this discussion, this will be termed the “spatial frequency”.        5. Spatial frequency spectrum: A spatial frequency spectrum is a representation of the spatial frequency content of a segment of a profile. It graphically illustrates the relative or absolute amounts of signal present in the segment as a function of spatial frequency.        6. Color frequency: A color frequency is a frequency of visible light, which has an associated wavelength, and therefore color, when viewed by a human on a display device. Color frequencies form a continuum of distinct colors.        7. Color frequency spectrum: For a given point on a display device, the final color displayed can be formed from a combination of one or more color frequencies. The color frequencies and their associated intensities (absolute and relative) determine the perceived color and brightness of that point on the display. Differing color spectra displayed at different points on the display device will therefore generally have differing perceived color and brightness, indicating differences in the underlying color frequency spectra.        8. Bin: When encoding spatial frequency spectra as color frequency spectra, groups of spatial frequencies may be grouped together by combination of their associated magnitudes (an example of this would be summation of the magnitudes), and each grouping of spatial frequencies forms what is termed a “bin”.        9. Color map: A color map is a one or more dimensional plot, in which color is assigned to points within the plot. In the case of the preferred embodiment described herein, this may take the form of a two-dimensional plot where the color frequencies are used to encode spatial frequencies.        
Biological structures can change size, shape or arrangement when changing from non-diseased to diseased states, or when changing between different functional modes. Many of these structures are of great clinical interest. For example, during angiogenesis, the number and spacing of blood vessels changes, and the blood vessels can take on a different arrangement to that in normal tissue. The ability to interrogate these structures rapidly and in-vivo is a highly desired capability. Similarly, physical structures, such as rock strata, can also experience change in structure, either spatially or temporally.
In the case of anatomical structures, for example mammary ducts or blood vessels, the sizes of many of these structures of interest can be very small and difficult or impossible to image using current in-vivo imaging techniques, for example Magnetic Resonance Imaging (MRI).
MRI, which is inherently a three-dimensional technique, is well suited to the determination of the structural details which will, in general, vary in three-dimensions. Thus, the ability to quantify and display these structures in three-dimensions is a desirable capability.
However, high resolution MRI images, which would be necessary to measure many smaller physical and biological structures, require careful patient positioning and stabilization, as well as lengthy exam times. Furthermore, high-field systems may be required. These high-field systems cost around $2 million and need to be housed in carefully controlled environments overseen by radiology specialists.
MRI is based on an extension of the mathematics of Fourier expansion which states that a one-dimensional repetitive waveform (e.g., a signal amplitude as a function of linear position) can be represented as the sum of a series of decreasing period (increasing frequency) sinusoidal waveforms with appropriate coefficients (k-values).
In MRI, the item (body part) to be imaged is a three-dimensional object. The basic concept of k-values in one dimension can be extended to two or three dimensions. Now, rather than a series of k-values, there is a two- or three-dimensional matrix of k-values, each k-value representing a particular spatial frequency and direction in the sample.
In Fourier analysis, converting from the k-values to the desired waveform (amplitude vs. time for a time-varying signal or image intensity vs. position for the MRI case) is accomplished by using a Fourier transform. The Fourier transform in simple terms is a well-known means to convert between the frequency domain and time domain (for time-varying signals). For images, as in the MRI case, the Fourier transform is used to convert between the spatial frequency domain (the series of sinusoidal waveforms and their coefficients, referred to as k-space) and the spatial arrangement of signal intensities for each of the imaged volumes (voxels). Similar to the case of time-varying signals, where the k-values are the coefficients for the sinusoidal waveforms with given periods, the k-values in the MRI case are the coefficients for the sinusoidal waveforms with given wave lengths (where the wavelengths are inversely related to spatial frequencies, i.e., a long wavelength is a low spatial frequency).
MRI technology today uses a number of methods to acquire images. Virtually all rely on gathering the k-space coefficients and later Fourier transforming them into an image (or set of images as in a 3D acquisition). In the simplest abstraction, this is accomplished by placing the part to be imaged in a strong magnetic field and exciting the hydrogen nuclei in the sample by transmitting at the sample a pulsed radio-frequency electromagnetic signal tuned to the resonant frequency of the hydrogen nuclei. This pulse starts the nuclei resonating at their resonant frequency. Then, to obtain information about where in the sample the signal originates from, the spins of the excited hydrogen atoms are encoded with a combination of phase and frequency encodes corresponding to the desired k-space data being acquired on that excitation. (Here phase and frequency correspond to the resonant frequency and phase of the hydrogen nuclei). This is accomplished by modulating the magnetic field spatially and temporally, so as to correspondingly spatially alter the resonant frequency of the nuclei and modulate their phase. A signal is received back then from the excited hydrogen nuclei of the sample, and the k-space values are extracted from the signal. This process of excitation, encoding, and signal acquisition is repeated until an entire matrix of k-space values (properly selected to constitute a Fourier series) is acquired with sufficiently high spatial frequency to resolve the desired features in the sample. Finally, the matrix of k-values is Fourier transformed to produce an image or images. There are many variations and extensions of this theme in use in current technology MRI systems. One approach utilizes frequency encoding to generate the k-values for each of these 2D slices.
In signal processing, techniques exist for measuring the spectral content of a time-varying signal, called time-frequency representations. One such example of this is a spectrogram. In a spectrogram, a plot of how the spectral density varies with time along the signal is generated. The spectral density measurement is created either by a series of bandpass filters, or more often by application of the short-time Fourier transform (SIFT) to the signal. In the case of the SIFT method, the spectral density displayed for a given time point is usually derived from the Fourier transform of a region (segment) extending over a period of time to either side of this.
U.S. Patent Application Publication Nos. US-2006/0155186-A1 and US-2007/0167717-A1 describe techniques for generating spatial frequency spectra from specific locations and directions in a three-dimensional sample using MRI techniques. These two patent applications are incorporated herein by reference.
U.S. Patent Application Publication No. US-2007/0167717-A1 describes a method for acquiring spatial frequency spectra from specific locations in a three-dimensional sample using modifications of the current MRI techniques for localized NMR spectroscopy. The innovation, in its simplest abstraction, is to add the use of a read out gradient to the current NMR spectroscopy pulse sequences and record the resultant echo for subsequent spatial frequency analysis. These techniques generate spectra from a selected region or generate an image of the results over a region of the sample. These methods can be applied to analyzing the structure of trabecular bone as well as for analyzing or diagnosing disease in cases where there is a difference in the spatial frequency power spectrum due to physiologic or disease processes. Various embodiments are disclosed. However, that application does not disclose the encoding of the spatial frequency power spectrum into color frequencies, which may then be plotted as a “color map”. That application also does not include application of this to one or more volumes within the sample to generate an image representation of a structure or structures of interest. Furthermore, that application does not include the use of multiple color maps running through the same region, but in different directions and/or at later time points, which would allow information about the anisotropy and time-evolution of a structure to be readily assessed and visualized in a desirable way.
Mapping of spatial frequency data is not currently performed, and thus there is no prior art.